Senior Statistician – AI Product Assurance

Caterpillar Inc.•Pittsburgh, PA

3d•$112,710 - $183,140•Onsite

About The Position

Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. The Senior Statistician – AI Product Assurance is responsible for providing rigorous statistical evidence to support the design, implementation and deployment of safety-related AI-enabled products. This role is part of a newly established AI Product Assurance team focused on demonstrating that complicated, complex, agentic based products meet defined safety objectives throughout their lifecycle. Statistical validation is a core element of the assurance approach, alongside functional safety and Safety of the Intended Functionality (SOTIF). The Senior Statistician serves as a technical authority, translating safety intent into quantitative evidence that can be reviewed, audited, and defended.

Requirements

Business Statistics: Knowledge of the statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions.
Significant experience applying statistical methods to complex, real-world systems where safety, risk, or reliability are critical.
Strong foundation in statistical inference, experimental design, and uncertainty analysis.
Experience with Bayesian methods, reliability engineering, rare-event estimation, and analysis of non-stationary data
Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms in order to write and modify programming languages.
Query and Database Access Tools: Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.
Accuracy and Attention to Detail: Understanding the necessity and value of accuracy; ability to complete tasks with high levels of precision.
Bachelor’s Degree in Statistics, Applied Mathematics, Data Science or related quantitate discipline.

Nice To Haves

Advanced Degree in Statistics, Applied Mathematics, Data Science or related quantitate discipline.
Experience working with AI, machine learning, autonomous, or agentic systems.
Familiarity with functional safety standards and/or SOTIF concepts.
Experience with Earth-Moving Machinery Products
Experience contributing statistical evidence to safety cases, assurance cases, or regulatory submissions.
Background in safety-critical industries such as autonomy, robotics, automotive, aerospace, industrial systems, or medical devices.
Demonstrated ability to communicate complex statistical concepts to non-statistical stakeholders.

Responsibilities

Lead the development and execution of statistical validation plans that support AI product safety claims for release and in-service operation.
Define quantitative safety metrics, acceptance criteria, and confidence levels aligned with internal governance and external standards.
Design and analyze experiments, simulations, and operational data sets to assess risk, uncertainty, and residual hazards, including rare-event and edge-case behavior.
Apply advanced statistical techniques such as Bayesian inference, reliability analysis, survival analysis, uncertainty quantification, and causal methods to complex systems.
Evaluate system performance under uncertainty, distributional shift, and changing operational conditions.
Collaborate with the engineering teams to integrate statistical evidence into safety cases and assurance artifacts.
Review and interpret large-scale test, simulation, and field data to identify safety-relevant trends, failure modes, and emergent behaviors in agentic systems.
Clearly document assumptions, limitations, and conclusions to support internal decision-making and external review.
Provide technical guidance and mentoring to engineers and analysts on sound statistical practices for safety-critical applications.
Support post-deployment monitoring, re-validation, and change impact assessment as AI systems evolve.